Examinando por Autor "Cuesta, Marina"

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A complexity measure for binary classification problems based on lost points
(Springer International Publishing, 2021) Lancho, Carmen; Martín de Diego, Isaac; Cuesta, Marina; Aceña, Víctor; M. Moguerza, Javier
Complexity measures are focused on exploring and capturing the complexity of a data set. In this paper, the Lost points (LP) complexity measure is proposed. It is obtained by applying k-means in a recursive and hierarchical way and it provides both the data set and the instance perspective. On the instance level, the LP measure gives a probability value for each point informing about the dominance of its class in its neighborhood. On the data set level, it estimates the proportion of lost points, referring to those points that are expected to be misclassified since they lie in areas where its class is not dominant. The proposed measure shows easily interpretable results competitive with measures from state-of-art. In addition, it provides probabilistic information useful to highlight the boundary decision on classification problems.
From classification to visualization: a two way trip
(Springer International Publishing, 2021) Cuesta, Marina; Martín de Diego, Isaac; Lancho, Carmen; Aceña, Víctor; M. Moguerza, Javier
High Dimensional Data (HDD) is one of the biggest challenges in Data Science arising from Big Data. The application of dimensionality reduction techniques over HDD allows visualization and, thus, a better problem understanding. In addition, these techniques also can enhance the performance of Machine Learning (ML) algorithms while increasing the explanatory power. This paper presents an automatic method capable of obtaining an adequate representation of the data, given a previously trained ML model. Likewise, an automatic method is introduced to bring a Support Vector Machine (SVM) model based on an adequate representation of the data. Both methods provide an Explanaible Machine Learning procedure. The proposal is tested on several data sets providing promising results. It significantly eases the visualization and understanding task to the data scientist when a ML model has already been trained, as well as the ML selection parameters when a reduced representation of data has been achieved.
Health Sufficiency Indicators for Pandemic Monitoring
(MDPI, 2021) M. Moguerza, Javier; Perelló Oliver, Salvador; Martín de Diego, Isaac; Aceña, Víctor; Lancho, Carmen; Cuesta, Marina; González Fernández, César
The outbreak of the COVID-19 disease, spreading all around the world and causing a worldwide pandemic, has lead to the collapse of the health systems of the most affected countries. Due to the ease of transmission, early prevention measures are proved to be fundamental to control the pandemic and, hence, the saturation of the health systems. Given the difficulty of obtaining characteristics of these systems of different countries and regions, it is necessary to define indicators based on basic information that enable the assessment of the evolution of the impact of a disease in a health system along with fair comparisons among different ones. This present paper introduces the Health Sufficiency Indicator (HSI), in its accumulated and daily versions. This indicator measures the additional pressure that a health care system has to deal with due to a pandemic. Hence, it allows to evaluate the capacity of a health system to give response to the corresponding needs arising from a pandemic and to compare the evolution of the disease among different regions. In addition, the Potential Occupancy Ratio (POR) in both its hospital ward bed and ICU bed versions is here introduced to asses the impact of the pandemic in the capacity of hospitals. These indicators and other well-known ones are applied to track the evolution of the impact of the disease on the Spanish health system during the first wave of the pandemic, both on national and regional levels. An international comparison among the most affected countries is also performed.
Hostility measure for multi-level study of data complexity
(Springer, 2022) Lancho, Carmen; Martín De Diego, Isaac; Cuesta, Marina; Aceña, Víctor; Moguerza, Javier M.
Complexity measures aim to characterize the underlying complexity of supervised data. These measures tackle factors hindering the performance of Machine Learning (ML) classifiers like overlap, density, linearity, etc. The state-of-the-art has mainly focused on the dataset perspective of complexity, i.e., offering an estimation of the complexity of the whole dataset. Recently, the instance perspective has also been addressed. In this paper, the hostility measure, a complexity measure offering a multi-level (instance, class, and dataset) perspective of data complexity is proposed. The proposal is built by estimating the novel notion of hostility: the difficulty of correctly classifying a point, a class, or a whole dataset given their corresponding neighborhoods. The proposed measure is estimated at the instance level by applying the k-means algorithm in a recursive and hierarchical way, which allows to analyze how points from different classes are naturally grouped together across partitions. The instance information is aggregated to provide complexity knowledge at the class and the dataset levels. The validity of the proposal is evaluated through a variety of experiments dealing with the three perspectives and the corresponding comparative with the state-of-the-art measures. Throughout the experiments, the hostility measure has shown promising results and to be competitive, stable, and robust.
Matemáticas Discreta y Álgebra. Teoría y práctica por y para La computación y la ciberseguridad
(Servicio de Publicaciones de la Universidad Rey Juan Carlos, 2022) Ruiz-Parrado, Victoria; Arias, Joaquín; Cuesta, Marina
La Matemática Discreta surge como una disciplina que unifica diversas áreas tradicionales de las Matemáticas (combinatoria, probabilidad, geometría de polígonos, aritmética, grafos, entre otros), como consecuencia, de ahí su interés en la informática, las telecomunicaciones, y en particular, la ciberseguridad. La información se manipula y almacena en los ordenadores en forma discreta (palabras formadas por ceros y unos), se necesita contar objetos (unidades de memorias, unidades de tiempo), se precisa estudiar relaciones entre conjuntos finitos (búsquedas en bases de datos), y es necesario analizar procesos que incluyan un número finito de pasos (algoritmos). La matemática discreta proporciona, por otro lado, algunas bases matemáticas para otros aspectos de la informática, como las estructuras de datos, algorítmica, bases de datos, teoría de autómatas, sistemas operativos y la investigación operativa. A su vez ayuda al desarrollo de ciertas capacidades fundamentales como la capacidad de formalizar, de razonar rigurosamente y/o de representar adecuadamente algunos conceptos. El Álgebra Lineal es, seguramente, una de las herramientas fundamentales en las Ciencias de la Computación. Originariamente dedicada a la resolución de sistemas de ecuaciones, su abstracción y formalismo la hacen a veces un poco árida de entender. Sin embargo la inmensidad de sus aplicaciones bien vale el esfuerzo: Teoría de la Información, Teoría de Códigos, Ecuaciones Diferenciales, Optimización, etc. Este manual combina Matemática Discreta y Álgebra Lineal, y es esencial para formar la base adecuada para comprender los modelos matemáticos que se presentan durante el desarrollo profesional en el campo de la informática y la ciberseguridad. Los objetivos que se buscan con este manual son aprender y utilizar técnicas y métodos propios de la Matemática Discreta y del Álgebra Lineal y su aplicación en el campo de la informática y la ciberseguridad. En concreto: Aprender métodos y conceptos básicos de algoritmos, aritmética modular, combinatoria y teoría de grafos. Discutir y resolver sistemas de ecuaciones lineales mediante el método de Gauss. Matrices y determinantes. Conocer la estructura de espacio vectorial, manejar la noción de aplicación lineal y su aplicación en diversos campos de la computación. Reconocer cuándo una matriz es diagonalizable y, si es así, saber diagonalizarla.
Padel two-dimensional tracking extraction from monocular video recordings
(Springer, 2024-11-14) Novillo, Álvaro; Aceña, Víctor; Lancho, Carmen; Cuesta, Marina; Martín de Diego, Isaac
This study introduces a novel framework for the automatic two-dimensional tracking of padel games using monocular recordings. By integrating advanced Computer Vision and Deep Learning techniques, our algorithm detects and tracks players, the court, and the ball. Through homography, we accurately project detected player positions onto a twodimensional court, enabling comprehensive tracking throughout the game. We tested the proposed algorithm using amateur video recordings of padel games found in literature. This approach remains user-friendly, cost-effective, and adaptable to various camera angles and lighting conditions. This makes it accessible to both amateur and professional players and coaches, providing a valuable tool for performance analysis. Additionally, the proposed framework holds potential for adaptation to other sports with minimal modifications, further broadening its applicability.